% vector cotaining number of features to be used for measuring
% classification accuracy changes
feat_num_vec = [80, 40, 20, 5, 3, 2];
accuracy = zeros(1, length(feat_num_vec));

for i = 1 : length(feat_num_vec)
%% Perform PCA

% number of features to select
feat_num = feat_num_vec(i);

C = cov(features);
[evec, eval] = eig(C);

eval = diag(eval);
[~, ind] = sort(-eval);

evec = evec(:, ind(1 : feat_num));

m = mean(features);

features = (features - ones(size(features, 1), 1)*m)*evec;

% perform PCA on testing set
testing_set = [img.car_xt; img.mot_xt];
testing_set = (testing_set - ones(size(testing_set, 1), 1)*m)*evec;

testing_answer = [zeros(1, length(r.cars_xt)), ones(1, length(r.moto_xt))];